Corporate Governance Practices in Senegalese Microfinance Institutions
Bibliographic record
Abstract
The objective of this paper is to analyse the corporate governance practices in Senegalese microfinance institutions (MFIs), in order to judge their effectiveness. The question is whether in this microfinance setting, governance structures are well implemented and function as well as in conventional firms. In the MFI's literature and specifically in an African environment, there are not too much studies assessing the state of corporate governance practices. This paper aims to provide insights, using the Senegalese MFIs’ setting. To reach this end, a survey is conducted over a sample of 99 MFIs. The exploratory factor analysis is used to measure our constructs, from a sand of items collected through a questionnaire. Once the constructs are built, a factorization is performed, and the Cronbach's Alpha (α) allows us the judge the effectiveness of the axes obtained. The results indicate that: - Board of directors (BODs) in Senegalese MFIs are characterized by a plurality of roles. These include a disciplinary, as well as an advisory role. - The composition of BODs in Senegalese MFIs and their mode of operation meet the optimality requirements. - Finally, the BODs in Senegalese MFIs display traits of competency, through two dimensions which are the general competence and the knowledge of the external environment. The practical implications of our results is that Senegalese MFIs have well-functioning BODs. Then being MFIs does not prevent adherence to the best corporate governance practices. The existence of well-functioning BODs should translate into improved financial and social performances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".